Comparison of alternative risk adjustment measures for predictive modeling: high risk patient case finding using Taiwan's National Health Insurance claims
Background Predictive modeling presents an opportunity to contain the expansion of medical expenditures by focusing on very few people. Evaluation of how risk adjustment models perform in predictive modeling in Taiwan or Asia has been rare. The aims of this study were to evaluate the performance of...
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| Published in | BMC health services research Vol. 10; no. 1; p. 343 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
London
BioMed Central
20.12.2010
BioMed Central Ltd Springer Nature B.V BMC |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1472-6963 1472-6963 |
| DOI | 10.1186/1472-6963-10-343 |
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| Abstract | Background
Predictive modeling presents an opportunity to contain the expansion of medical expenditures by focusing on very few people. Evaluation of how risk adjustment models perform in predictive modeling in Taiwan or Asia has been rare. The aims of this study were to evaluate the performance of different risk adjustment models (the ACG risk adjustment system and prior expenditures) in predictive modeling, using Taiwan's National Health Insurance (NHI) claims data, and to compare characteristics of potentially high-expenditure subjects identified through different models.
Methods
A random sample of NHI enrollees continuously enrolled in 2002 and 2003 (n = 164,562) was selected. Health status measures and total expenditures derived from 2002 NHI claims data were used to predict the possibility of becoming 2003 top users. Statistics-based indicators (C-statistics, sensitivity, & Predictive Positive Value) and characteristics of identified top groups by different models (expenditures and prevalence of manageable diseases) were presented.
Results
Both diagnosis-based and prior expenditures models performed much better than the demographic model. Diagnosis-based models were better in identifying top users with manageable diseases; prior expenditures models were better in statistics-based indicators and identifying people with higher average expenditures. Prior expenditures status could correctly identify more actual top users than diagnosis-based or demographic models. The proportions of actual top users that could be identified by diagnosis-based models alone were much lower than that identified by prior expenditures status.
Conclusions
Predicted top users identified by different models have different characteristics and there is little agreement between modes regarding which groups would be potentially top users; therefore, which model to use should depend on the purpose of predictive modeling. Prior expenditures are a more powerful tool than diagnosis-based risk adjusters in terms of correctly identifying more actual high expenditures users. There is still much room left for improvement of diagnosis-based models in predictive modeling. |
|---|---|
| AbstractList | Background Predictive modeling presents an opportunity to contain the expansion of medical expenditures by focusing on very few people. Evaluation of how risk adjustment models perform in predictive modeling in Taiwan or Asia has been rare. The aims of this study were to evaluate the performance of different risk adjustment models (the ACG risk adjustment system and prior expenditures) in predictive modeling, using Taiwan's National Health Insurance (NHI) claims data, and to compare characteristics of potentially high-expenditure subjects identified through different models. Methods A random sample of NHI enrollees continuously enrolled in 2002 and 2003 (n = 164,562) was selected. Health status measures and total expenditures derived from 2002 NHI claims data were used to predict the possibility of becoming 2003 top users. Statistics-based indicators (C-statistics, sensitivity, and Predictive Positive Value) and characteristics of identified top groups by different models (expenditures and prevalence of manageable diseases) were presented. Results Both diagnosis-based and prior expenditures models performed much better than the demographic model. Diagnosis-based models were better in identifying top users with manageable diseases; prior expenditures models were better in statistics-based indicators and identifying people with higher average expenditures. Prior expenditures status could correctly identify more actual top users than diagnosis-based or demographic models. The proportions of actual top users that could be identified by diagnosis-based models alone were much lower than that identified by prior expenditures status. Conclusions Predicted top users identified by different models have different characteristics and there is little agreement between modes regarding which groups would be potentially top users; therefore, which model to use should depend on the purpose of predictive modeling. Prior expenditures are a more powerful tool than diagnosis-based risk adjusters in terms of correctly identifying more actual high expenditures users. There is still much room left for improvement of diagnosis-based models in predictive modeling. Predictive modeling presents an opportunity to contain the expansion of medical expenditures by focusing on very few people. Evaluation of how risk adjustment models perform in predictive modeling in Taiwan or Asia has been rare. The aims of this study were to evaluate the performance of different risk adjustment models (the ACG risk adjustment system and prior expenditures) in predictive modeling, using Taiwan's National Health Insurance (NHI) claims data, and to compare characteristics of potentially high-expenditure subjects identified through different models.BACKGROUNDPredictive modeling presents an opportunity to contain the expansion of medical expenditures by focusing on very few people. Evaluation of how risk adjustment models perform in predictive modeling in Taiwan or Asia has been rare. The aims of this study were to evaluate the performance of different risk adjustment models (the ACG risk adjustment system and prior expenditures) in predictive modeling, using Taiwan's National Health Insurance (NHI) claims data, and to compare characteristics of potentially high-expenditure subjects identified through different models.A random sample of NHI enrollees continuously enrolled in 2002 and 2003 (n = 164,562) was selected. Health status measures and total expenditures derived from 2002 NHI claims data were used to predict the possibility of becoming 2003 top users. Statistics-based indicators (C-statistics, sensitivity, & Predictive Positive Value) and characteristics of identified top groups by different models (expenditures and prevalence of manageable diseases) were presented.METHODSA random sample of NHI enrollees continuously enrolled in 2002 and 2003 (n = 164,562) was selected. Health status measures and total expenditures derived from 2002 NHI claims data were used to predict the possibility of becoming 2003 top users. Statistics-based indicators (C-statistics, sensitivity, & Predictive Positive Value) and characteristics of identified top groups by different models (expenditures and prevalence of manageable diseases) were presented.Both diagnosis-based and prior expenditures models performed much better than the demographic model. Diagnosis-based models were better in identifying top users with manageable diseases; prior expenditures models were better in statistics-based indicators and identifying people with higher average expenditures. Prior expenditures status could correctly identify more actual top users than diagnosis-based or demographic models. The proportions of actual top users that could be identified by diagnosis-based models alone were much lower than that identified by prior expenditures status.RESULTSBoth diagnosis-based and prior expenditures models performed much better than the demographic model. Diagnosis-based models were better in identifying top users with manageable diseases; prior expenditures models were better in statistics-based indicators and identifying people with higher average expenditures. Prior expenditures status could correctly identify more actual top users than diagnosis-based or demographic models. The proportions of actual top users that could be identified by diagnosis-based models alone were much lower than that identified by prior expenditures status.Predicted top users identified by different models have different characteristics and there is little agreement between modes regarding which groups would be potentially top users; therefore, which model to use should depend on the purpose of predictive modeling. Prior expenditures are a more powerful tool than diagnosis-based risk adjusters in terms of correctly identifying more actual high expenditures users. There is still much room left for improvement of diagnosis-based models in predictive modeling.CONCLUSIONSPredicted top users identified by different models have different characteristics and there is little agreement between modes regarding which groups would be potentially top users; therefore, which model to use should depend on the purpose of predictive modeling. Prior expenditures are a more powerful tool than diagnosis-based risk adjusters in terms of correctly identifying more actual high expenditures users. There is still much room left for improvement of diagnosis-based models in predictive modeling. Abstract Background: Predictive modeling presents an opportunity to contain the expansion of medical expenditures by focusing on very few people. Evaluation of how risk adjustment models perform in predictive modeling in Taiwan or Asia has been rare. The aims of this study were to evaluate the performance of different risk adjustment models (the ACG risk adjustment system and prior expenditures) in predictive modeling, using Taiwan's National Health Insurance (NHI) claims data, and to compare characteristics of potentially high-expenditure subjects identified through different models. Methods: A random sample of NHI enrollees continuously enrolled in 2002 and 2003 (n = 164,562) was selected. Health status measures and total expenditures derived from 2002 NHI claims data were used to predict the possibility of becoming 2003 top users. Statistics-based indicators (C-statistics, sensitivity, & Predictive Positive Value) and characteristics of identified top groups by different models (expenditures and prevalence of manageable diseases) were presented. Results: Both diagnosis-based and prior expenditures models performed much better than the demographic model. Diagnosis-based models were better in identifying top users with manageable diseases; prior expenditures models were better in statistics-based indicators and identifying people with higher average expenditures. Prior expenditures status could correctly identify more actual top users than diagnosis-based or demographic models. The proportions of actual top users that could be identified by diagnosis-based models alone were much lower than that identified by prior expenditures status. Conclusions: Predicted top users identified by different models have different characteristics and there is little agreement between modes regarding which groups would be potentially top users; therefore, which model to use should depend on the purpose of predictive modeling. Prior expenditures are a more powerful tool than diagnosis-based risk adjusters in terms of correctly identifying more actual high expenditures users. There is still much room left for improvement of diagnosis-based models in predictive modeling. Predictive modeling presents an opportunity to contain the expansion of medical expenditures by focusing on very few people. Evaluation of how risk adjustment models perform in predictive modeling in Taiwan or Asia has been rare. The aims of this study were to evaluate the performance of different risk adjustment models (the ACG risk adjustment system and prior expenditures) in predictive modeling, using Taiwan's National Health Insurance (NHI) claims data, and to compare characteristics of potentially high-expenditure subjects identified through different models. A random sample of NHI enrollees continuously enrolled in 2002 and 2003 (n = 164,562) was selected. Health status measures and total expenditures derived from 2002 NHI claims data were used to predict the possibility of becoming 2003 top users. Statistics-based indicators (C-statistics, sensitivity, and Predictive Positive Value) and characteristics of identified top groups by different models (expenditures and prevalence of manageable diseases) were presented. Both diagnosis-based and prior expenditures models performed much better than the demographic model. Diagnosis-based models were better in identifying top users with manageable diseases; prior expenditures models were better in statistics-based indicators and identifying people with higher average expenditures. Prior expenditures status could correctly identify more actual top users than diagnosis-based or demographic models. The proportions of actual top users that could be identified by diagnosis-based models alone were much lower than that identified by prior expenditures status. Predicted top users identified by different models have different characteristics and there is little agreement between modes regarding which groups would be potentially top users; therefore, which model to use should depend on the purpose of predictive modeling. Prior expenditures are a more powerful tool than diagnosis-based risk adjusters in terms of correctly identifying more actual high expenditures users. There is still much room left for improvement of diagnosis-based models in predictive modeling. Background Predictive modeling presents an opportunity to contain the expansion of medical expenditures by focusing on very few people. Evaluation of how risk adjustment models perform in predictive modeling in Taiwan or Asia has been rare. The aims of this study were to evaluate the performance of different risk adjustment models (the ACG risk adjustment system and prior expenditures) in predictive modeling, using Taiwan's National Health Insurance (NHI) claims data, and to compare characteristics of potentially high-expenditure subjects identified through different models. Methods A random sample of NHI enrollees continuously enrolled in 2002 and 2003 (n = 164,562) was selected. Health status measures and total expenditures derived from 2002 NHI claims data were used to predict the possibility of becoming 2003 top users. Statistics-based indicators (C-statistics, sensitivity, & Predictive Positive Value) and characteristics of identified top groups by different models (expenditures and prevalence of manageable diseases) were presented. Results Both diagnosis-based and prior expenditures models performed much better than the demographic model. Diagnosis-based models were better in identifying top users with manageable diseases; prior expenditures models were better in statistics-based indicators and identifying people with higher average expenditures. Prior expenditures status could correctly identify more actual top users than diagnosis-based or demographic models. The proportions of actual top users that could be identified by diagnosis-based models alone were much lower than that identified by prior expenditures status. Conclusions Predicted top users identified by different models have different characteristics and there is little agreement between modes regarding which groups would be potentially top users; therefore, which model to use should depend on the purpose of predictive modeling. Prior expenditures are a more powerful tool than diagnosis-based risk adjusters in terms of correctly identifying more actual high expenditures users. There is still much room left for improvement of diagnosis-based models in predictive modeling. Predictive modeling presents an opportunity to contain the expansion of medical expenditures by focusing on very few people. Evaluation of how risk adjustment models perform in predictive modeling in Taiwan or Asia has been rare. The aims of this study were to evaluate the performance of different risk adjustment models (the ACG risk adjustment system and prior expenditures) in predictive modeling, using Taiwan's National Health Insurance (NHI) claims data, and to compare characteristics of potentially high-expenditure subjects identified through different models. A random sample of NHI enrollees continuously enrolled in 2002 and 2003 (n = 164,562) was selected. Health status measures and total expenditures derived from 2002 NHI claims data were used to predict the possibility of becoming 2003 top users. Statistics-based indicators (C-statistics, sensitivity, & Predictive Positive Value) and characteristics of identified top groups by different models (expenditures and prevalence of manageable diseases) were presented. Both diagnosis-based and prior expenditures models performed much better than the demographic model. Diagnosis-based models were better in identifying top users with manageable diseases; prior expenditures models were better in statistics-based indicators and identifying people with higher average expenditures. Prior expenditures status could correctly identify more actual top users than diagnosis-based or demographic models. The proportions of actual top users that could be identified by diagnosis-based models alone were much lower than that identified by prior expenditures status. Predicted top users identified by different models have different characteristics and there is little agreement between modes regarding which groups would be potentially top users; therefore, which model to use should depend on the purpose of predictive modeling. Prior expenditures are a more powerful tool than diagnosis-based risk adjusters in terms of correctly identifying more actual high expenditures users. There is still much room left for improvement of diagnosis-based models in predictive modeling. |
| ArticleNumber | 343 |
| Audience | Academic |
| Author | Chang, Hsien-Yen Weiner, Jonathan P Lee, Wui-Chiang |
| AuthorAffiliation | 1 Department of Health Policy & Management, Bloomberg School of Public Health, Johns Hopkins University, 624 N. Broadway, Baltimore, MD 21205, USA 2 Department of Medical Affairs & Planning, Taipei Veterans General Hospital, and Institute of Hospital Administration & Management, School of Medicine, National Yang-Ming University, 201 Section 2, Shih-Pai Rd, Taipei City 11217, Taiwan |
| AuthorAffiliation_xml | – name: 1 Department of Health Policy & Management, Bloomberg School of Public Health, Johns Hopkins University, 624 N. Broadway, Baltimore, MD 21205, USA – name: 2 Department of Medical Affairs & Planning, Taipei Veterans General Hospital, and Institute of Hospital Administration & Management, School of Medicine, National Yang-Ming University, 201 Section 2, Shih-Pai Rd, Taipei City 11217, Taiwan |
| Author_xml | – sequence: 1 givenname: Hsien-Yen surname: Chang fullname: Chang, Hsien-Yen email: hchang2@jhsph.edu organization: Department of Health Policy & Management, Bloomberg School of Public Health, Johns Hopkins University – sequence: 2 givenname: Wui-Chiang surname: Lee fullname: Lee, Wui-Chiang organization: Department of Medical Affairs & Planning, Taipei Veterans General Hospital, and Institute of Hospital Administration & Management, School of Medicine, National Yang-Ming University – sequence: 3 givenname: Jonathan P surname: Weiner fullname: Weiner, Jonathan P organization: Department of Health Policy & Management, Bloomberg School of Public Health, Johns Hopkins University |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/21172009$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1186/1472-6963-8-153 10.1097/00005650-199105000-00006 10.1089/dis.2005.8.48 10.2165/00115677-200513020-00005 10.1089/dis.2005.8.42 10.1097/00005650-200410000-00012 10.2165/00115677-200311060-00005 10.3200/HTPS.83.3.17-24 10.1097/00005650-199908000-00011 10.1089/109350702760301448 10.1016/S1726-4901(08)70103-5 10.1186/1741-7015-8-7 10.1097/01.MLR.0000094480.13057.75 10.1377/hlthaff.20.2.9 |
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| References | Pietz, Ashton, McDonell, Wray (CR2) 2004; 42 Cousins, Shickle, Bander (CR4) 2002; 5 Radcliff, Cote, Duncan (CR14) 2005; 83 Meenan, O'Keeffe-Rosetti, Hornbrook, Bachman, Goodman, Fishman, Hurtado (CR15) 1999; 37 Lee (CR17) 2008; 8 Zhao, Ash, Haughton, McMillan (CR10) 2003; 11 Ridinger, Rice (CR6) 2000; 21 Lee, Huang (CR18) 2008; 71 Ash, Zhao, Ellis, Schlein Kramer (CR3) 2001; 36 Berk, Monheit (CR5) 2001; 20 Hu, Root (CR1) 2005; 8 Rosen, Wang, Montez, Rakovski, Berlowitz, Lucove (CR9) 2005; 13 Weng (CR11) 2005; 8 (CR7) 2005 Weiner, Starfield, Steinwachs, Mumford (CR13) 1991; 29 Chang, Weiner (CR16) 2010; 8 Starfield, Weiner, Mumford, Steinwachs (CR12) 1991; 26 Meenan, Goodman, Fishman, Hornbrook, O'Keeffe-Rosetti, Bachman (CR8) 2003; 41 B Starfield (1489_CR12) 1991; 26 AK Rosen (1489_CR9) 2005; 13 RT Meenan (1489_CR15) 1999; 37 K Pietz (1489_CR2) 2004; 42 AS Ash (1489_CR3) 2001; 36 HC Weng (1489_CR11) 2005; 8 TA Radcliff (1489_CR14) 2005; 83 Health Services Research & Development Center at The Johns Hopkins University Bloomberg School of Public Health (1489_CR7) 2005 MH Ridinger (1489_CR6) 2000; 21 RT Meenan (1489_CR8) 2003; 41 HY Chang (1489_CR16) 2010; 8 Y Zhao (1489_CR10) 2003; 11 MS Cousins (1489_CR4) 2002; 5 G Hu (1489_CR1) 2005; 8 ML Berk (1489_CR5) 2001; 20 JP Weiner (1489_CR13) 1991; 29 WC Lee (1489_CR17) 2008; 8 WC Lee (1489_CR18) 2008; 71 14583693 - Med Care. 2003 Nov;41(11):1301-12 15722703 - Dis Manag. 2005 Feb;8(1):42-7 10448724 - Med Care. 1999 Aug;37(8):815-23 10787539 - Health Manag Technol. 2000 Feb;21(2):10-2 18436502 - J Chin Med Assoc. 2008 Apr;71(4):191-9 15722704 - Dis Manag. 2005 Feb;8(1):48-58 16294676 - Hosp Top. 2005 Summer;83(3):17-24 11260963 - Health Aff (Millwood). 2001 Mar-Apr;20(2):9-18 15377936 - Med Care. 2004 Oct;42(10):1027-35 20082689 - BMC Med. 2010;8:7 1902278 - Med Care. 1991 May;29(5):452-72 1901841 - Health Serv Res. 1991 Apr;26(1):53-74 16148969 - Health Serv Res. 2001 Dec;36(6 Pt 2):194-206 18644140 - BMC Health Serv Res. 2008;8:153 |
| References_xml | – volume: 8 start-page: 153 year: 2008 ident: CR17 article-title: Quantifying morbidities by Adjusted Clinical Group system for a Taiwan population: a nationwide analysis publication-title: BMC Health Serv Res doi: 10.1186/1472-6963-8-153 – volume: 29 start-page: 452 issue: 5 year: 1991 end-page: 472 ident: CR13 article-title: Development and application of a population-oriented measure of ambulatory care case-mix publication-title: Med Care doi: 10.1097/00005650-199105000-00006 – volume: 8 start-page: 48 issue: 1 year: 2005 end-page: 58 ident: CR11 article-title: Impacts of a government-sponsored outpatient-based disease management program for patients with asthma: a preliminary analysis of national data from Taiwan publication-title: Dis Manag doi: 10.1089/dis.2005.8.48 – volume: 13 start-page: 117 issue: 2 year: 2005 end-page: 127 ident: CR9 article-title: Identifying future high-healthcare users: exploring the value of diagnostic and prior utilization information publication-title: Dis Manage Health Outcomes doi: 10.2165/00115677-200513020-00005 – volume: 26 start-page: 53 issue: 1 year: 1991 end-page: 74 ident: CR12 article-title: Ambulatory care groups: a categorization of diagnoses for research and management publication-title: Health Serv Res – volume: 8 start-page: 42 issue: 1 year: 2005 end-page: 47 ident: CR1 article-title: Accuracy of prediction models in the context of disease management publication-title: Dis Manag doi: 10.1089/dis.2005.8.42 – volume: 42 start-page: 1027 issue: 10 year: 2004 end-page: 1035 ident: CR2 article-title: Predicting healthcare costs in a population of veterans affairs beneficiaries using diagnosis-based risk adjustment and self-reported health status publication-title: Med Care doi: 10.1097/00005650-200410000-00012 – volume: 11 start-page: 389 issue: 6 year: 2003 end-page: 397 ident: CR10 article-title: Identifying future high-cost cases through predictive modeling publication-title: Dis Manage Health Outcomes doi: 10.2165/00115677-200311060-00005 – volume: 83 start-page: 17 issue: 3 year: 2005 end-page: 24 ident: CR14 article-title: The identification of high-cost patients publication-title: Hosp Top doi: 10.3200/HTPS.83.3.17-24 – volume: 37 start-page: 815 issue: 8 year: 1999 end-page: 823 ident: CR15 article-title: The sensitivity and specificity of forecasting high-cost users of medical care publication-title: Med Care doi: 10.1097/00005650-199908000-00011 – volume: 36 start-page: 194 issue: 6 Pt 2 year: 2001 end-page: 206 ident: CR3 article-title: Finding future high-cost cases: comparing prior cost versus diagnosis-based methods publication-title: Health Serv Res – volume: 5 start-page: 157 issue: 3 year: 2002 end-page: 167 ident: CR4 article-title: An introduction to predictive modeling for disease management risk stratification publication-title: Dis Manag doi: 10.1089/109350702760301448 – volume: 71 start-page: 191 issue: 4 year: 2008 end-page: 199 ident: CR18 article-title: Explanatory ability of the ACG system regarding the utilization and expenditure of the national health insurance population in Taiwan--a 5-year analysis publication-title: J Chin Med Assoc doi: 10.1016/S1726-4901(08)70103-5 – volume: 21 start-page: 10 issue: 2 year: 2000 end-page: 12 ident: CR6 article-title: Predictive modeling points way to future risk status publication-title: Health Manag Technol – year: 2005 ident: CR7 publication-title: The Johns Hopkins ACG Case-Mix System Reference Manual Version 7.0 – volume: 8 start-page: 7 issue: 1 year: 2010 ident: CR16 article-title: An in-depth assessment of a diagnosis-based risk adjustment model based on national health insurance claims: the application of the Johns Hopkins Adjusted Clinical Group case-mix system in Taiwan publication-title: BMC Med doi: 10.1186/1741-7015-8-7 – volume: 41 start-page: 1301 issue: 11 year: 2003 end-page: 1312 ident: CR8 article-title: Using risk-adjustment models to identify high-cost risks publication-title: Med Care doi: 10.1097/01.MLR.0000094480.13057.75 – volume: 20 start-page: 9 issue: 2 year: 2001 end-page: 18 ident: CR5 article-title: The concentration of health care expenditures, revisited publication-title: Health Aff (Millwood) doi: 10.1377/hlthaff.20.2.9 – volume: 42 start-page: 1027 issue: 10 year: 2004 ident: 1489_CR2 publication-title: Med Care doi: 10.1097/00005650-200410000-00012 – volume: 11 start-page: 389 issue: 6 year: 2003 ident: 1489_CR10 publication-title: Dis Manage Health Outcomes doi: 10.2165/00115677-200311060-00005 – volume: 36 start-page: 194 issue: 6 Pt 2 year: 2001 ident: 1489_CR3 publication-title: Health Serv Res – volume: 41 start-page: 1301 issue: 11 year: 2003 ident: 1489_CR8 publication-title: Med Care doi: 10.1097/01.MLR.0000094480.13057.75 – volume: 13 start-page: 117 issue: 2 year: 2005 ident: 1489_CR9 publication-title: Dis Manage Health Outcomes doi: 10.2165/00115677-200513020-00005 – volume: 5 start-page: 157 issue: 3 year: 2002 ident: 1489_CR4 publication-title: Dis Manag doi: 10.1089/109350702760301448 – volume: 83 start-page: 17 issue: 3 year: 2005 ident: 1489_CR14 publication-title: Hosp Top doi: 10.3200/HTPS.83.3.17-24 – volume: 21 start-page: 10 issue: 2 year: 2000 ident: 1489_CR6 publication-title: Health Manag Technol – volume: 8 start-page: 42 issue: 1 year: 2005 ident: 1489_CR1 publication-title: Dis Manag doi: 10.1089/dis.2005.8.42 – volume: 71 start-page: 191 issue: 4 year: 2008 ident: 1489_CR18 publication-title: J Chin Med Assoc doi: 10.1016/S1726-4901(08)70103-5 – volume: 26 start-page: 53 issue: 1 year: 1991 ident: 1489_CR12 publication-title: Health Serv Res – volume: 8 start-page: 7 issue: 1 year: 2010 ident: 1489_CR16 publication-title: BMC Med doi: 10.1186/1741-7015-8-7 – volume-title: The Johns Hopkins ACG Case-Mix System Reference Manual Version 7.0 year: 2005 ident: 1489_CR7 – volume: 20 start-page: 9 issue: 2 year: 2001 ident: 1489_CR5 publication-title: Health Aff (Millwood) doi: 10.1377/hlthaff.20.2.9 – volume: 8 start-page: 153 year: 2008 ident: 1489_CR17 publication-title: BMC Health Serv Res doi: 10.1186/1472-6963-8-153 – volume: 8 start-page: 48 issue: 1 year: 2005 ident: 1489_CR11 publication-title: Dis Manag doi: 10.1089/dis.2005.8.48 – volume: 37 start-page: 815 issue: 8 year: 1999 ident: 1489_CR15 publication-title: Med Care doi: 10.1097/00005650-199908000-00011 – volume: 29 start-page: 452 issue: 5 year: 1991 ident: 1489_CR13 publication-title: Med Care doi: 10.1097/00005650-199105000-00006 – reference: 15722703 - Dis Manag. 2005 Feb;8(1):42-7 – reference: 1901841 - Health Serv Res. 1991 Apr;26(1):53-74 – reference: 18644140 - BMC Health Serv Res. 2008;8:153 – reference: 14583693 - Med Care. 2003 Nov;41(11):1301-12 – reference: 16148969 - Health Serv Res. 2001 Dec;36(6 Pt 2):194-206 – reference: 18436502 - J Chin Med Assoc. 2008 Apr;71(4):191-9 – reference: 1902278 - Med Care. 1991 May;29(5):452-72 – reference: 15722704 - Dis Manag. 2005 Feb;8(1):48-58 – reference: 16294676 - Hosp Top. 2005 Summer;83(3):17-24 – reference: 11260963 - Health Aff (Millwood). 2001 Mar-Apr;20(2):9-18 – reference: 20082689 - BMC Med. 2010;8:7 – reference: 10448724 - Med Care. 1999 Aug;37(8):815-23 – reference: 10787539 - Health Manag Technol. 2000 Feb;21(2):10-2 – reference: 15377936 - Med Care. 2004 Oct;42(10):1027-35 |
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| Snippet | Background
Predictive modeling presents an opportunity to contain the expansion of medical expenditures by focusing on very few people. Evaluation of how risk... Predictive modeling presents an opportunity to contain the expansion of medical expenditures by focusing on very few people. Evaluation of how risk adjustment... Background Predictive modeling presents an opportunity to contain the expansion of medical expenditures by focusing on very few people. Evaluation of how risk... Abstract Background: Predictive modeling presents an opportunity to contain the expansion of medical expenditures by focusing on very few people. Evaluation of... Abstract Background Predictive modeling presents an opportunity to contain the expansion of medical expenditures by focusing on very few people. Evaluation of... |
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| Title | Comparison of alternative risk adjustment measures for predictive modeling: high risk patient case finding using Taiwan's National Health Insurance claims |
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